Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations565
Missing cells2448
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory194.3 KiB
Average record size in memory352.2 B

Variable types

Numeric15
Categorical20
Text9

Alerts

age has constant value "unknown" Constant
selection has constant value "unknown" Constant
release_date has constant value "unknown" Constant
key_change_10 has constant value "unknown" Constant
country is highly overall correlated with direct_qualifier_10 and 2 other fieldsHigh correlation
direct_qualifier_10 is highly overall correlated with country and 8 other fieldsHigh correlation
favourite_10 is highly overall correlated with final_televote_points and 2 other fieldsHigh correlation
final_draw_position is highly overall correlated with direct_qualifier_10 and 1 other fieldsHigh correlation
final_jury_points is highly overall correlated with final_jury_votes and 6 other fieldsHigh correlation
final_jury_votes is highly overall correlated with final_jury_points and 5 other fieldsHigh correlation
final_place is highly overall correlated with final_jury_points and 8 other fieldsHigh correlation
final_televote_points is highly overall correlated with favourite_10 and 6 other fieldsHigh correlation
final_televote_votes is highly overall correlated with final_jury_points and 6 other fieldsHigh correlation
final_total_points is highly overall correlated with favourite_10 and 9 other fieldsHigh correlation
host_10 is highly overall correlated with race and 4 other fieldsHigh correlation
instrumentalness is highly overall correlated with raceHigh correlation
qualified_10 is highly overall correlated with country and 8 other fieldsHigh correlation
race is highly overall correlated with favourite_10 and 8 other fieldsHigh correlation
semi_draw_position is highly overall correlated with direct_qualifier_10 and 2 other fieldsHigh correlation
semi_final is highly overall correlated with country and 3 other fieldsHigh correlation
semi_jury_points is highly overall correlated with direct_qualifier_10 and 7 other fieldsHigh correlation
semi_place is highly overall correlated with direct_qualifier_10 and 11 other fieldsHigh correlation
semi_televote_points is highly overall correlated with direct_qualifier_10 and 9 other fieldsHigh correlation
semi_total_points is highly overall correlated with direct_qualifier_10 and 7 other fieldsHigh correlation
instrumentalness is highly imbalanced (76.1%) Imbalance
favourite_10 is highly imbalanced (86.4%) Imbalance
race is highly imbalanced (95.2%) Imbalance
host_10 is highly imbalanced (83.2%) Imbalance
final_draw_position has 11 (1.9%) missing values Missing
loudness has 26 (4.6%) missing values Missing
instrumentalness has 29 (5.1%) missing values Missing
final_televote_points has 236 (41.8%) missing values Missing
final_jury_points has 236 (41.8%) missing values Missing
final_televote_votes has 327 (57.9%) missing values Missing
final_jury_votes has 327 (57.9%) missing values Missing
final_place has 207 (36.6%) missing values Missing
final_total_points has 207 (36.6%) missing values Missing
semi_place has 82 (14.5%) missing values Missing
semi_televote_points has 322 (57.0%) missing values Missing
semi_jury_points has 353 (62.5%) missing values Missing
semi_total_points has 82 (14.5%) missing values Missing
backing_dancers has 378 (66.9%) zeros Zeros
backing_singers has 362 (64.1%) zeros Zeros
backing_instruments has 386 (68.3%) zeros Zeros
final_televote_points has 9 (1.6%) zeros Zeros
final_televote_votes has 10 (1.8%) zeros Zeros
semi_televote_points has 7 (1.2%) zeros Zeros

Reproduction

Analysis started2024-10-31 19:55:25.441331
Analysis finished2024-10-31 19:56:03.531535
Duration38.09 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

Distinct14
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.6531
Minimum2009
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:03.637390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2016
Q32019
95-th percentile2023
Maximum2023
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.312691
Coefficient of variation (CV)0.0021395998
Kurtosis-1.1348455
Mean2015.6531
Median Absolute Deviation (MAD)3
Skewness0.14105187
Sum1138844
Variance18.599303
MonotonicityDecreasing
2024-10-31T20:56:03.787980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2018 43
 
7.6%
2011 43
 
7.6%
2017 42
 
7.4%
2016 42
 
7.4%
2012 42
 
7.4%
2009 42
 
7.4%
2019 41
 
7.3%
2022 40
 
7.1%
2021 39
 
6.9%
2015 39
 
6.9%
Other values (4) 152
26.9%
ValueCountFrequency (%)
2009 42
7.4%
2010 39
6.9%
2011 43
7.6%
2012 42
7.4%
2013 39
6.9%
2014 37
6.5%
2015 39
6.9%
2016 42
7.4%
2017 42
7.4%
2018 43
7.6%
ValueCountFrequency (%)
2023 37
6.5%
2022 40
7.1%
2021 39
6.9%
2019 41
7.3%
2018 43
7.6%
2017 42
7.4%
2016 42
7.4%
2015 39
6.9%
2014 37
6.5%
2013 39
6.9%

semi_final
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2
245 
1
240 
-
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 245
43.4%
1 240
42.5%
- 80
 
14.2%

Length

2024-10-31T20:56:03.945796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:04.091142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 245
43.4%
1 240
42.5%
80
 
14.2%

Most occurring characters

ValueCountFrequency (%)
2 245
43.4%
1 240
42.5%
- 80
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 245
43.4%
1 240
42.5%
- 80
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 245
43.4%
1 240
42.5%
- 80
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 245
43.4%
1 240
42.5%
- 80
 
14.2%

semi_draw_position
Categorical

High correlation 

Distinct20
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
-
80 
9
 
28
3
 
28
4
 
28
5
 
28
Other values (15)
373 

Length

Max length2
Median length1
Mean length1.4123894
Min length1

Characters and Unicode

Total characters798
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
- 80
 
14.2%
9 28
 
5.0%
3 28
 
5.0%
4 28
 
5.0%
5 28
 
5.0%
6 28
 
5.0%
7 28
 
5.0%
1 28
 
5.0%
8 28
 
5.0%
11 28
 
5.0%
Other values (10) 233
41.2%

Length

2024-10-31T20:56:04.249630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80
 
14.2%
11 28
 
5.0%
10 28
 
5.0%
15 28
 
5.0%
14 28
 
5.0%
13 28
 
5.0%
12 28
 
5.0%
9 28
 
5.0%
2 28
 
5.0%
8 28
 
5.0%
Other values (10) 233
41.2%

Most occurring characters

ValueCountFrequency (%)
1 289
36.2%
- 80
 
10.0%
3 56
 
7.0%
4 56
 
7.0%
5 56
 
7.0%
2 56
 
7.0%
6 54
 
6.8%
7 49
 
6.1%
8 42
 
5.3%
9 32
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 289
36.2%
- 80
 
10.0%
3 56
 
7.0%
4 56
 
7.0%
5 56
 
7.0%
2 56
 
7.0%
6 54
 
6.8%
7 49
 
6.1%
8 42
 
5.3%
9 32
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 289
36.2%
- 80
 
10.0%
3 56
 
7.0%
4 56
 
7.0%
5 56
 
7.0%
2 56
 
7.0%
6 54
 
6.8%
7 49
 
6.1%
8 42
 
5.3%
9 32
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 289
36.2%
- 80
 
10.0%
3 56
 
7.0%
4 56
 
7.0%
5 56
 
7.0%
2 56
 
7.0%
6 54
 
6.8%
7 49
 
6.1%
8 42
 
5.3%
9 32
 
4.0%

final_draw_position
Categorical

High correlation  Missing 

Distinct28
Distinct (%)5.1%
Missing11
Missing (%)1.9%
Memory size4.5 KiB
-
196 
7
 
14
5
 
14
2
 
14
25
 
14
Other values (23)
302 

Length

Max length2
Median length1
Mean length1.4187726
Min length1

Characters and Unicode

Total characters786
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row20
2nd row5
3rd row2
4th row25
5th row3

Common Values

ValueCountFrequency (%)
- 196
34.7%
7 14
 
2.5%
5 14
 
2.5%
2 14
 
2.5%
25 14
 
2.5%
3 14
 
2.5%
23 14
 
2.5%
18 14
 
2.5%
9 14
 
2.5%
14 14
 
2.5%
Other values (18) 232
41.1%

Length

2024-10-31T20:56:04.406531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
196
35.4%
16 14
 
2.5%
4 14
 
2.5%
21 14
 
2.5%
19 14
 
2.5%
11 14
 
2.5%
8 14
 
2.5%
6 14
 
2.5%
15 14
 
2.5%
22 14
 
2.5%
Other values (18) 232
41.9%

Most occurring characters

ValueCountFrequency (%)
- 196
24.9%
1 180
22.9%
2 134
17.0%
5 42
 
5.3%
3 42
 
5.3%
4 42
 
5.3%
6 37
 
4.7%
7 29
 
3.7%
8 28
 
3.6%
9 28
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 196
24.9%
1 180
22.9%
2 134
17.0%
5 42
 
5.3%
3 42
 
5.3%
4 42
 
5.3%
6 37
 
4.7%
7 29
 
3.7%
8 28
 
3.6%
9 28
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 196
24.9%
1 180
22.9%
2 134
17.0%
5 42
 
5.3%
3 42
 
5.3%
4 42
 
5.3%
6 37
 
4.7%
7 29
 
3.7%
8 28
 
3.6%
9 28
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 196
24.9%
1 180
22.9%
2 134
17.0%
5 42
 
5.3%
3 42
 
5.3%
4 42
 
5.3%
6 37
 
4.7%
7 29
 
3.7%
8 28
 
3.6%
9 28
 
3.6%

country
Categorical

High correlation 

Distinct47
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Norway
 
14
Germany
 
14
Netherlands
 
14
Lithuania
 
14
Sweden
 
14
Other values (42)
495 

Length

Max length22
Median length15
Mean length7.7522124
Min length5

Characters and Unicode

Total characters4380
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowNorway
2nd rowMalta
3rd rowSerbia
4th rowLatvia
5th rowPortugal

Common Values

ValueCountFrequency (%)
Norway 14
 
2.5%
Germany 14
 
2.5%
Netherlands 14
 
2.5%
Lithuania 14
 
2.5%
Sweden 14
 
2.5%
Moldova 14
 
2.5%
Switzerland 14
 
2.5%
Azerbaijan 14
 
2.5%
United Kingdom 14
 
2.5%
Ireland 14
 
2.5%
Other values (37) 425
75.2%

Length

2024-10-31T20:56:04.588236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
norway 14
 
2.2%
israel 14
 
2.2%
albania 14
 
2.2%
spain 14
 
2.2%
slovenia 14
 
2.2%
malta 14
 
2.2%
greece 14
 
2.2%
iceland 14
 
2.2%
belgium 14
 
2.2%
estonia 14
 
2.2%
Other values (43) 483
77.5%

Most occurring characters

ValueCountFrequency (%)
a 624
14.2%
n 384
 
8.8%
e 378
 
8.6%
i 345
 
7.9%
r 323
 
7.4%
l 231
 
5.3%
o 219
 
5.0%
t 176
 
4.0%
d 157
 
3.6%
u 127
 
2.9%
Other values (32) 1416
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 624
14.2%
n 384
 
8.8%
e 378
 
8.6%
i 345
 
7.9%
r 323
 
7.4%
l 231
 
5.3%
o 219
 
5.0%
t 176
 
4.0%
d 157
 
3.6%
u 127
 
2.9%
Other values (32) 1416
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 624
14.2%
n 384
 
8.8%
e 378
 
8.6%
i 345
 
7.9%
r 323
 
7.4%
l 231
 
5.3%
o 219
 
5.0%
t 176
 
4.0%
d 157
 
3.6%
u 127
 
2.9%
Other values (32) 1416
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 624
14.2%
n 384
 
8.8%
e 378
 
8.6%
i 345
 
7.9%
r 323
 
7.4%
l 231
 
5.3%
o 219
 
5.0%
t 176
 
4.0%
d 157
 
3.6%
u 127
 
2.9%
Other values (32) 1416
32.3%
Distinct552
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:04.868872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length50
Median length34
Mean length12.345133
Min length3

Characters and Unicode

Total characters6975
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique540 ?
Unique (%)95.6%

Sample

1st rowAlessandra
2nd rowThe Busker
3rd rowLuke Black
4th rowSudden Lights
5th rowMimicat
ValueCountFrequency (%)
the 10
 
1.0%
nina 5
 
0.5%
valentina 4
 
0.4%
michael 4
 
0.4%
monika 4
 
0.4%
4
 
0.4%
monetta 3
 
0.3%
elena 3
 
0.3%
pasha 3
 
0.3%
anna 3
 
0.3%
Other values (889) 943
95.6%
2024-10-31T20:56:05.338226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 825
 
11.8%
e 510
 
7.3%
i 498
 
7.1%
n 462
 
6.6%
421
 
6.0%
o 392
 
5.6%
r 345
 
4.9%
l 295
 
4.2%
s 245
 
3.5%
t 216
 
3.1%
Other values (57) 2766
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 825
 
11.8%
e 510
 
7.3%
i 498
 
7.1%
n 462
 
6.6%
421
 
6.0%
o 392
 
5.6%
r 345
 
4.9%
l 295
 
4.2%
s 245
 
3.5%
t 216
 
3.1%
Other values (57) 2766
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 825
 
11.8%
e 510
 
7.3%
i 498
 
7.1%
n 462
 
6.6%
421
 
6.0%
o 392
 
5.6%
r 345
 
4.9%
l 295
 
4.2%
s 245
 
3.5%
t 216
 
3.1%
Other values (57) 2766
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 825
 
11.8%
e 510
 
7.3%
i 498
 
7.1%
n 462
 
6.6%
421
 
6.0%
o 392
 
5.6%
r 345
 
4.9%
l 295
 
4.2%
s 245
 
3.5%
t 216
 
3.1%
Other values (57) 2766
39.7%
Distinct547
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:05.661125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length46
Median length26
Mean length11.729204
Min length2

Characters and Unicode

Total characters6627
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique533 ?
Unique (%)94.3%

Sample

1st rowQueen of Kings
2nd rowDance (Our Own Party)
3rd rowSamo mi se spava
4th rowAija
5th rowAi cora��o
ValueCountFrequency (%)
the 35
 
2.6%
love 32
 
2.4%
of 25
 
1.9%
you 24
 
1.8%
me 24
 
1.8%
my 22
 
1.6%
i 21
 
1.6%
a 21
 
1.6%
is 15
 
1.1%
in 15
 
1.1%
Other values (762) 1109
82.6%
2024-10-31T20:56:06.137772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
778
 
11.7%
e 684
 
10.3%
a 462
 
7.0%
o 443
 
6.7%
i 366
 
5.5%
n 327
 
4.9%
t 327
 
4.9%
r 313
 
4.7%
l 220
 
3.3%
s 213
 
3.2%
Other values (61) 2494
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
778
 
11.7%
e 684
 
10.3%
a 462
 
7.0%
o 443
 
6.7%
i 366
 
5.5%
n 327
 
4.9%
t 327
 
4.9%
r 313
 
4.7%
l 220
 
3.3%
s 213
 
3.2%
Other values (61) 2494
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
778
 
11.7%
e 684
 
10.3%
a 462
 
7.0%
o 443
 
6.7%
i 366
 
5.5%
n 327
 
4.9%
t 327
 
4.9%
r 313
 
4.7%
l 220
 
3.3%
s 213
 
3.2%
Other values (61) 2494
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
778
 
11.7%
e 684
 
10.3%
a 462
 
7.0%
o 443
 
6.7%
i 366
 
5.5%
n 327
 
4.9%
t 327
 
4.9%
r 313
 
4.7%
l 220
 
3.3%
s 213
 
3.2%
Other values (61) 2494
37.6%
Distinct75
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:06.345830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length42
Median length7
Mean length8.3132743
Min length5

Characters and Unicode

Total characters4697
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)6.5%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowSerbian, English
4th rowEnglish
5th rowPortuguese
ValueCountFrequency (%)
english 413
69.2%
french 16
 
2.7%
italian 15
 
2.5%
spanish 13
 
2.2%
portuguese 10
 
1.7%
serbian 9
 
1.5%
albanian 7
 
1.2%
romanian 6
 
1.0%
slovene 6
 
1.0%
macedonian 5
 
0.8%
Other values (53) 97
 
16.2%
2024-10-31T20:56:06.736993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 643
13.7%
i 579
12.3%
s 495
10.5%
l 492
10.5%
h 488
10.4%
g 472
10.0%
E 445
9.5%
a 187
 
4.0%
e 122
 
2.6%
r 95
 
2.0%
Other values (36) 679
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 643
13.7%
i 579
12.3%
s 495
10.5%
l 492
10.5%
h 488
10.4%
g 472
10.0%
E 445
9.5%
a 187
 
4.0%
e 122
 
2.6%
r 95
 
2.0%
Other values (36) 679
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 643
13.7%
i 579
12.3%
s 495
10.5%
l 492
10.5%
h 488
10.4%
g 472
10.0%
E 445
9.5%
a 187
 
4.0%
e 122
 
2.6%
r 95
 
2.0%
Other values (36) 679
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 643
13.7%
i 579
12.3%
s 495
10.5%
l 492
10.5%
h 488
10.4%
g 472
10.0%
E 445
9.5%
a 187
 
4.0%
e 122
 
2.6%
r 95
 
2.0%
Other values (36) 679
14.5%

style
Categorical

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Pop
271 
Ballad
148 
Dance
55 
Rock
47 
Traditional
41 

Length

Max length11
Median length6
Mean length4.6548673
Min length3

Characters and Unicode

Total characters2630
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPop
2nd rowPop
3rd rowPop
4th rowRock
5th rowPop

Common Values

ValueCountFrequency (%)
Pop 271
48.0%
Ballad 148
26.2%
Dance 55
 
9.7%
Rock 47
 
8.3%
Traditional 41
 
7.3%
Opera 3
 
0.5%

Length

2024-10-31T20:56:06.933053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:07.097716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pop 271
48.0%
ballad 148
26.2%
dance 55
 
9.7%
rock 47
 
8.3%
traditional 41
 
7.3%
opera 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 436
16.6%
o 359
13.7%
l 337
12.8%
p 274
10.4%
P 271
10.3%
d 189
7.2%
B 148
 
5.6%
c 102
 
3.9%
n 96
 
3.7%
i 82
 
3.1%
Other values (8) 336
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 436
16.6%
o 359
13.7%
l 337
12.8%
p 274
10.4%
P 271
10.3%
d 189
7.2%
B 148
 
5.6%
c 102
 
3.9%
n 96
 
3.7%
i 82
 
3.1%
Other values (8) 336
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 436
16.6%
o 359
13.7%
l 337
12.8%
p 274
10.4%
P 271
10.3%
d 189
7.2%
B 148
 
5.6%
c 102
 
3.9%
n 96
 
3.7%
i 82
 
3.1%
Other values (8) 336
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 436
16.6%
o 359
13.7%
l 337
12.8%
p 274
10.4%
P 271
10.3%
d 189
7.2%
B 148
 
5.6%
c 102
 
3.9%
n 96
 
3.7%
i 82
 
3.1%
Other values (8) 336
12.8%

direct_qualifier_10
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
276 
-
209 
1
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row-
3rd row0
4th row-
5th row0

Common Values

ValueCountFrequency (%)
0 276
48.8%
- 209
37.0%
1 80
 
14.2%

Length

2024-10-31T20:56:07.252581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:07.387840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 276
48.8%
209
37.0%
1 80
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 276
48.8%
- 209
37.0%
1 80
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 276
48.8%
- 209
37.0%
1 80
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 276
48.8%
- 209
37.0%
1 80
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 276
48.8%
- 209
37.0%
1 80
 
14.2%

gender
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Female
274 
Male
244 
Mix
47 

Length

Max length6
Median length4
Mean length4.8867257
Min length3

Characters and Unicode

Total characters2761
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 274
48.5%
Male 244
43.2%
Mix 47
 
8.3%

Length

2024-10-31T20:56:07.540567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:07.688565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 274
48.5%
male 244
43.2%
mix 47
 
8.3%

Most occurring characters

ValueCountFrequency (%)
e 792
28.7%
a 518
18.8%
l 518
18.8%
M 291
 
10.5%
F 274
 
9.9%
m 274
 
9.9%
i 47
 
1.7%
x 47
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 792
28.7%
a 518
18.8%
l 518
18.8%
M 291
 
10.5%
F 274
 
9.9%
m 274
 
9.9%
i 47
 
1.7%
x 47
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 792
28.7%
a 518
18.8%
l 518
18.8%
M 291
 
10.5%
F 274
 
9.9%
m 274
 
9.9%
i 47
 
1.7%
x 47
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 792
28.7%
a 518
18.8%
l 518
18.8%
M 291
 
10.5%
F 274
 
9.9%
m 274
 
9.9%
i 47
 
1.7%
x 47
 
1.7%

main_singers
Real number (ℝ)

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2973451
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:07.815919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.85473858
Coefficient of variation (CV)0.6588367
Kurtosis15.570723
Mean1.2973451
Median Absolute Deviation (MAD)0
Skewness3.782892
Sum733
Variance0.73057805
MonotonicityNot monotonic
2024-10-31T20:56:07.947889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 473
83.7%
2 57
 
10.1%
3 15
 
2.7%
6 9
 
1.6%
4 8
 
1.4%
5 3
 
0.5%
ValueCountFrequency (%)
1 473
83.7%
2 57
 
10.1%
3 15
 
2.7%
4 8
 
1.4%
5 3
 
0.5%
6 9
 
1.6%
ValueCountFrequency (%)
6 9
 
1.6%
5 3
 
0.5%
4 8
 
1.4%
3 15
 
2.7%
2 57
 
10.1%
1 473
83.7%

age
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
unknown
565 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 565
100.0%

Length

2024-10-31T20:56:08.098241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:08.218090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 565
100.0%

Most occurring characters

ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

selection
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
unknown
565 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 565
100.0%

Length

2024-10-31T20:56:08.352027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:08.477778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 565
100.0%

Most occurring characters

ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

key
Categorical

Distinct30
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
C Major
48 
A Minor
 
31
E Minor
 
30
B Minor
 
30
D Minor
 
29
Other values (25)
397 

Length

Max length8
Median length7
Mean length7.0477876
Min length1

Characters and Unicode

Total characters3982
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowE Minor
2nd rowF Minor
3rd rowA Major
4th rowA Minor
5th rowFs Minor

Common Values

ValueCountFrequency (%)
C Major 48
 
8.5%
A Minor 31
 
5.5%
E Minor 30
 
5.3%
B Minor 30
 
5.3%
D Minor 29
 
5.1%
F Minor 29
 
5.1%
- 26
 
4.6%
G Major 26
 
4.6%
D Major 26
 
4.6%
G Minor 24
 
4.2%
Other values (20) 266
47.1%

Length

2024-10-31T20:56:08.622985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
minor 294
26.6%
major 245
22.2%
c 70
 
6.3%
d 55
 
5.0%
a 53
 
4.8%
g 50
 
4.5%
f 45
 
4.1%
e 43
 
3.9%
b 40
 
3.6%
fs 40
 
3.6%
Other values (9) 169
15.3%

Most occurring characters

ValueCountFrequency (%)
M 539
13.5%
o 539
13.5%
r 539
13.5%
539
13.5%
n 294
7.4%
i 294
7.4%
a 245
6.2%
j 245
6.2%
b 122
 
3.1%
A 92
 
2.3%
Other values (8) 534
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 539
13.5%
o 539
13.5%
r 539
13.5%
539
13.5%
n 294
7.4%
i 294
7.4%
a 245
6.2%
j 245
6.2%
b 122
 
3.1%
A 92
 
2.3%
Other values (8) 534
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 539
13.5%
o 539
13.5%
r 539
13.5%
539
13.5%
n 294
7.4%
i 294
7.4%
a 245
6.2%
j 245
6.2%
b 122
 
3.1%
A 92
 
2.3%
Other values (8) 534
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 539
13.5%
o 539
13.5%
r 539
13.5%
539
13.5%
n 294
7.4%
i 294
7.4%
a 245
6.2%
j 245
6.2%
b 122
 
3.1%
A 92
 
2.3%
Other values (8) 534
13.4%

BPM
Text

Distinct102
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:08.861179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.6548673
Min length1

Characters and Unicode

Total characters1500
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)3.4%

Sample

1st row110
2nd row103
3rd row103
4th row160
5th row145
ValueCountFrequency (%)
128 28
 
5.0%
120 27
 
4.8%
26
 
4.6%
130 25
 
4.4%
125 14
 
2.5%
126 13
 
2.3%
90 12
 
2.1%
122 12
 
2.1%
132 12
 
2.1%
123 10
 
1.8%
Other values (92) 386
68.3%
2024-10-31T20:56:09.288364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 466
31.1%
2 190
12.7%
0 175
 
11.7%
8 128
 
8.5%
3 114
 
7.6%
4 88
 
5.9%
5 86
 
5.7%
9 81
 
5.4%
7 81
 
5.4%
6 65
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 466
31.1%
2 190
12.7%
0 175
 
11.7%
8 128
 
8.5%
3 114
 
7.6%
4 88
 
5.9%
5 86
 
5.7%
9 81
 
5.4%
7 81
 
5.4%
6 65
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 466
31.1%
2 190
12.7%
0 175
 
11.7%
8 128
 
8.5%
3 114
 
7.6%
4 88
 
5.9%
5 86
 
5.7%
9 81
 
5.4%
7 81
 
5.4%
6 65
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 466
31.1%
2 190
12.7%
0 175
 
11.7%
8 128
 
8.5%
3 114
 
7.6%
4 88
 
5.9%
5 86
 
5.7%
9 81
 
5.4%
7 81
 
5.4%
6 65
 
4.3%

energy
Text

Distinct80
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:09.517954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.9522124
Min length1

Characters and Unicode

Total characters1103
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.4%

Sample

1st row36
2nd row78
3rd row70
4th row55
5th row63
ValueCountFrequency (%)
26
 
4.6%
70 17
 
3.0%
76 16
 
2.8%
87 16
 
2.8%
91 16
 
2.8%
64 14
 
2.5%
67 14
 
2.5%
75 13
 
2.3%
92 13
 
2.3%
79 13
 
2.3%
Other values (70) 407
72.0%
2024-10-31T20:56:10.536704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 165
15.0%
6 156
14.1%
8 155
14.1%
9 115
10.4%
4 107
9.7%
5 106
9.6%
3 78
7.1%
2 75
6.8%
1 63
 
5.7%
0 57
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 165
15.0%
6 156
14.1%
8 155
14.1%
9 115
10.4%
4 107
9.7%
5 106
9.6%
3 78
7.1%
2 75
6.8%
1 63
 
5.7%
0 57
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 165
15.0%
6 156
14.1%
8 155
14.1%
9 115
10.4%
4 107
9.7%
5 106
9.6%
3 78
7.1%
2 75
6.8%
1 63
 
5.7%
0 57
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 165
15.0%
6 156
14.1%
8 155
14.1%
9 115
10.4%
4 107
9.7%
5 106
9.6%
3 78
7.1%
2 75
6.8%
1 63
 
5.7%
0 57
 
5.2%
Distinct71
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:10.754061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9539823
Min length1

Characters and Unicode

Total characters1104
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)1.6%

Sample

1st row64
2nd row70
3rd row56
4th row56
5th row66
ValueCountFrequency (%)
26
 
4.6%
52 20
 
3.5%
66 20
 
3.5%
56 18
 
3.2%
63 16
 
2.8%
58 16
 
2.8%
57 15
 
2.7%
68 15
 
2.7%
53 15
 
2.7%
50 14
 
2.5%
Other values (61) 390
69.0%
2024-10-31T20:56:11.131420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 193
17.5%
6 192
17.4%
4 138
12.5%
7 127
11.5%
3 113
10.2%
2 79
7.2%
8 78
7.1%
1 55
 
5.0%
0 53
 
4.8%
9 50
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 193
17.5%
6 192
17.4%
4 138
12.5%
7 127
11.5%
3 113
10.2%
2 79
7.2%
8 78
7.1%
1 55
 
5.0%
0 53
 
4.8%
9 50
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 193
17.5%
6 192
17.4%
4 138
12.5%
7 127
11.5%
3 113
10.2%
2 79
7.2%
8 78
7.1%
1 55
 
5.0%
0 53
 
4.8%
9 50
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 193
17.5%
6 192
17.4%
4 138
12.5%
7 127
11.5%
3 113
10.2%
2 79
7.2%
8 78
7.1%
1 55
 
5.0%
0 53
 
4.8%
9 50
 
4.5%
Distinct89
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:11.369800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9380531
Min length1

Characters and Unicode

Total characters1095
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23
2nd row82
3rd row11
4th row40
5th row77
ValueCountFrequency (%)
26
 
4.6%
30 13
 
2.3%
23 12
 
2.1%
21 12
 
2.1%
34 12
 
2.1%
48 11
 
1.9%
33 11
 
1.9%
36 10
 
1.8%
54 10
 
1.8%
39 10
 
1.8%
Other values (79) 438
77.5%
2024-10-31T20:56:11.770721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 155
14.2%
2 151
13.8%
4 141
12.9%
5 115
10.5%
1 113
10.3%
6 100
9.1%
7 97
8.9%
8 85
7.8%
9 63
5.8%
0 49
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 155
14.2%
2 151
13.8%
4 141
12.9%
5 115
10.5%
1 113
10.3%
6 100
9.1%
7 97
8.9%
8 85
7.8%
9 63
5.8%
0 49
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 155
14.2%
2 151
13.8%
4 141
12.9%
5 115
10.5%
1 113
10.3%
6 100
9.1%
7 97
8.9%
8 85
7.8%
9 63
5.8%
0 49
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 155
14.2%
2 151
13.8%
4 141
12.9%
5 115
10.5%
1 113
10.3%
6 100
9.1%
7 97
8.9%
8 85
7.8%
9 63
5.8%
0 49
 
4.5%

loudness
Categorical

Missing 

Distinct16
Distinct (%)3.0%
Missing26
Missing (%)4.6%
Memory size4.5 KiB
5 dB
115 
6 dB
98 
4 dB
97 
7 dB
69 
8 dB
54 
Other values (11)
106 

Length

Max length5
Median length4
Mean length4.0890538
Min length4

Characters and Unicode

Total characters2204
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.7%

Sample

1st row10 dB
2nd row6 dB
3rd row10 dB
4th row8 dB
5th row8 dB

Common Values

ValueCountFrequency (%)
5 dB 115
20.4%
6 dB 98
17.3%
4 dB 97
17.2%
7 dB 69
12.2%
8 dB 54
9.6%
3 dB 28
 
5.0%
10 dB 26
 
4.6%
9 dB 23
 
4.1%
11 dB 10
 
1.8%
2 dB 7
 
1.2%
Other values (6) 12
 
2.1%
(Missing) 26
 
4.6%

Length

2024-10-31T20:56:11.953405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
db 539
50.0%
5 115
 
10.7%
6 98
 
9.1%
4 97
 
9.0%
7 69
 
6.4%
8 54
 
5.0%
3 28
 
2.6%
10 26
 
2.4%
9 23
 
2.1%
11 10
 
0.9%
Other values (7) 19
 
1.8%

Most occurring characters

ValueCountFrequency (%)
539
24.5%
d 539
24.5%
B 539
24.5%
5 116
 
5.3%
6 99
 
4.5%
4 98
 
4.4%
7 69
 
3.1%
1 58
 
2.6%
8 55
 
2.5%
3 30
 
1.4%
Other values (3) 62
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
539
24.5%
d 539
24.5%
B 539
24.5%
5 116
 
5.3%
6 99
 
4.5%
4 98
 
4.4%
7 69
 
3.1%
1 58
 
2.6%
8 55
 
2.5%
3 30
 
1.4%
Other values (3) 62
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
539
24.5%
d 539
24.5%
B 539
24.5%
5 116
 
5.3%
6 99
 
4.5%
4 98
 
4.4%
7 69
 
3.1%
1 58
 
2.6%
8 55
 
2.5%
3 30
 
1.4%
Other values (3) 62
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
539
24.5%
d 539
24.5%
B 539
24.5%
5 116
 
5.3%
6 99
 
4.5%
4 98
 
4.4%
7 69
 
3.1%
1 58
 
2.6%
8 55
 
2.5%
3 30
 
1.4%
Other values (3) 62
 
2.8%
Distinct82
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:12.145201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.4867257
Min length1

Characters and Unicode

Total characters840
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)1.2%

Sample

1st row58
2nd row2
3rd row4
4th row5
5th row31
ValueCountFrequency (%)
0 74
 
13.1%
1 58
 
10.3%
38
 
6.7%
2 29
 
5.1%
4 22
 
3.9%
5 19
 
3.4%
6 14
 
2.5%
11 13
 
2.3%
3 12
 
2.1%
14 12
 
2.1%
Other values (72) 274
48.5%
2024-10-31T20:56:12.520347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 166
19.8%
2 109
13.0%
0 108
12.9%
4 81
9.6%
5 72
8.6%
3 67
8.0%
6 64
 
7.6%
8 62
 
7.4%
7 45
 
5.4%
- 38
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 166
19.8%
2 109
13.0%
0 108
12.9%
4 81
9.6%
5 72
8.6%
3 67
8.0%
6 64
 
7.6%
8 62
 
7.4%
7 45
 
5.4%
- 38
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 166
19.8%
2 109
13.0%
0 108
12.9%
4 81
9.6%
5 72
8.6%
3 67
8.0%
6 64
 
7.6%
8 62
 
7.4%
7 45
 
5.4%
- 38
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 166
19.8%
2 109
13.0%
0 108
12.9%
4 81
9.6%
5 72
8.6%
3 67
8.0%
6 64
 
7.6%
8 62
 
7.4%
7 45
 
5.4%
- 38
 
4.5%

instrumentalness
Categorical

High correlation  Imbalance  Missing 

Distinct29
Distinct (%)5.4%
Missing29
Missing (%)5.1%
Memory size4.5 KiB
0
458 
-
 
26
1
 
7
2
 
6
79
 
4
Other values (24)
 
35

Length

Max length2
Median length1
Mean length1.0578358
Min length1

Characters and Unicode

Total characters567
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)2.8%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 458
81.1%
- 26
 
4.6%
1 7
 
1.2%
2 6
 
1.1%
79 4
 
0.7%
3 3
 
0.5%
23 3
 
0.5%
92 2
 
0.4%
4 2
 
0.4%
39 2
 
0.4%
Other values (19) 23
 
4.1%
(Missing) 29
 
5.1%

Length

2024-10-31T20:56:12.700485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 458
85.4%
26
 
4.9%
1 7
 
1.3%
2 6
 
1.1%
79 4
 
0.7%
3 3
 
0.6%
23 3
 
0.6%
6 2
 
0.4%
89 2
 
0.4%
81 2
 
0.4%
Other values (19) 23
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 460
81.1%
- 26
 
4.6%
2 14
 
2.5%
8 13
 
2.3%
9 12
 
2.1%
3 12
 
2.1%
1 11
 
1.9%
7 8
 
1.4%
4 5
 
0.9%
6 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 460
81.1%
- 26
 
4.6%
2 14
 
2.5%
8 13
 
2.3%
9 12
 
2.1%
3 12
 
2.1%
1 11
 
1.9%
7 8
 
1.4%
4 5
 
0.9%
6 4
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 460
81.1%
- 26
 
4.6%
2 14
 
2.5%
8 13
 
2.3%
9 12
 
2.1%
3 12
 
2.1%
1 11
 
1.9%
7 8
 
1.4%
4 5
 
0.9%
6 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 460
81.1%
- 26
 
4.6%
2 14
 
2.5%
8 13
 
2.3%
9 12
 
2.1%
3 12
 
2.1%
1 11
 
1.9%
7 8
 
1.4%
4 5
 
0.9%
6 4
 
0.7%
Distinct60
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:12.879072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.7362832
Min length1

Characters and Unicode

Total characters981
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)2.5%

Sample

1st row10
2nd row18
3rd row32
4th row8
5th row16
ValueCountFrequency (%)
10 51
 
9.0%
11 41
 
7.3%
9 37
 
6.5%
8 31
 
5.5%
26
 
4.6%
12 25
 
4.4%
7 24
 
4.2%
14 24
 
4.2%
13 19
 
3.4%
6 18
 
3.2%
Other values (50) 269
47.6%
2024-10-31T20:56:13.213457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 293
29.9%
2 126
12.8%
3 120
12.2%
0 71
 
7.2%
8 64
 
6.5%
9 60
 
6.1%
6 57
 
5.8%
4 56
 
5.7%
7 54
 
5.5%
5 54
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 293
29.9%
2 126
12.8%
3 120
12.2%
0 71
 
7.2%
8 64
 
6.5%
9 60
 
6.1%
6 57
 
5.8%
4 56
 
5.7%
7 54
 
5.5%
5 54
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 293
29.9%
2 126
12.8%
3 120
12.2%
0 71
 
7.2%
8 64
 
6.5%
9 60
 
6.1%
6 57
 
5.8%
4 56
 
5.7%
7 54
 
5.5%
5 54
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 293
29.9%
2 126
12.8%
3 120
12.2%
0 71
 
7.2%
8 64
 
6.5%
9 60
 
6.1%
6 57
 
5.8%
4 56
 
5.7%
7 54
 
5.5%
5 54
 
5.5%

speechiness
Categorical

Distinct28
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
3
154 
4
141 
5
79 
6
38 
7
29 
Other values (23)
124 

Length

Max length2
Median length1
Mean length1.0955752
Min length1

Characters and Unicode

Total characters619
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)1.6%

Sample

1st row3
2nd row4
3rd row5
4th row7
5th row5

Common Values

ValueCountFrequency (%)
3 154
27.3%
4 141
25.0%
5 79
14.0%
6 38
 
6.7%
7 29
 
5.1%
8 26
 
4.6%
- 26
 
4.6%
9 14
 
2.5%
10 10
 
1.8%
14 7
 
1.2%
Other values (18) 41
 
7.3%

Length

2024-10-31T20:56:13.400202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 154
27.3%
4 141
25.0%
5 79
14.0%
6 38
 
6.7%
7 29
 
5.1%
8 26
 
4.6%
26
 
4.6%
9 14
 
2.5%
10 10
 
1.8%
14 7
 
1.2%
Other values (18) 41
 
7.3%

Most occurring characters

ValueCountFrequency (%)
3 163
26.3%
4 151
24.4%
5 82
13.2%
1 54
 
8.7%
6 43
 
6.9%
7 31
 
5.0%
8 26
 
4.2%
- 26
 
4.2%
2 17
 
2.7%
9 16
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 163
26.3%
4 151
24.4%
5 82
13.2%
1 54
 
8.7%
6 43
 
6.9%
7 31
 
5.0%
8 26
 
4.2%
- 26
 
4.2%
2 17
 
2.7%
9 16
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 163
26.3%
4 151
24.4%
5 82
13.2%
1 54
 
8.7%
6 43
 
6.9%
7 31
 
5.0%
8 26
 
4.2%
- 26
 
4.2%
2 17
 
2.7%
9 16
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 163
26.3%
4 151
24.4%
5 82
13.2%
1 54
 
8.7%
6 43
 
6.9%
7 31
 
5.0%
8 26
 
4.2%
- 26
 
4.2%
2 17
 
2.7%
9 16
 
2.6%

release_date
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
unknown
565 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 565
100.0%

Length

2024-10-31T20:56:13.559039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:13.689114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 565
100.0%

Most occurring characters

ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

key_change_10
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
unknown
565 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3955
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 565
100.0%

Length

2024-10-31T20:56:13.822780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:13.951544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 565
100.0%

Most occurring characters

ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1695
42.9%
u 565
 
14.3%
k 565
 
14.3%
o 565
 
14.3%
w 565
 
14.3%

backing_dancers
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88495575
Minimum0
Maximum5
Zeros378
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:14.055689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.460789
Coefficient of variation (CV)1.6506916
Kurtosis0.67558262
Mean0.88495575
Median Absolute Deviation (MAD)0
Skewness1.4403108
Sum500
Variance2.1339045
MonotonicityNot monotonic
2024-10-31T20:56:14.189940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 378
66.9%
4 50
 
8.8%
2 47
 
8.3%
1 45
 
8.0%
3 32
 
5.7%
5 13
 
2.3%
ValueCountFrequency (%)
0 378
66.9%
1 45
 
8.0%
2 47
 
8.3%
3 32
 
5.7%
4 50
 
8.8%
5 13
 
2.3%
ValueCountFrequency (%)
5 13
 
2.3%
4 50
 
8.8%
3 32
 
5.7%
2 47
 
8.3%
1 45
 
8.0%
0 378
66.9%

backing_singers
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0884956
Minimum0
Maximum5
Zeros362
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:14.330116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6240519
Coefficient of variation (CV)1.4920152
Kurtosis-0.20901866
Mean1.0884956
Median Absolute Deviation (MAD)0
Skewness1.1326478
Sum615
Variance2.6375447
MonotonicityNot monotonic
2024-10-31T20:56:14.470274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 362
64.1%
3 56
 
9.9%
4 49
 
8.7%
2 49
 
8.7%
5 26
 
4.6%
1 23
 
4.1%
ValueCountFrequency (%)
0 362
64.1%
1 23
 
4.1%
2 49
 
8.7%
3 56
 
9.9%
4 49
 
8.7%
5 26
 
4.6%
ValueCountFrequency (%)
5 26
 
4.6%
4 49
 
8.7%
3 56
 
9.9%
2 49
 
8.7%
1 23
 
4.1%
0 362
64.1%

backing_instruments
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83716814
Minimum0
Maximum5
Zeros386
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:14.610252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4604839
Coefficient of variation (CV)1.7445527
Kurtosis1.3270599
Mean0.83716814
Median Absolute Deviation (MAD)0
Skewness1.6244893
Sum473
Variance2.1330132
MonotonicityNot monotonic
2024-10-31T20:56:14.747876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 386
68.3%
1 54
 
9.6%
3 35
 
6.2%
2 34
 
6.0%
4 34
 
6.0%
5 22
 
3.9%
ValueCountFrequency (%)
0 386
68.3%
1 54
 
9.6%
2 34
 
6.0%
3 35
 
6.2%
4 34
 
6.0%
5 22
 
3.9%
ValueCountFrequency (%)
5 22
 
3.9%
4 34
 
6.0%
3 35
 
6.2%
2 34
 
6.0%
1 54
 
9.6%
0 386
68.3%

instrument_10
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
498 
1
67 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 498
88.1%
1 67
 
11.9%

Length

2024-10-31T20:56:14.896298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:15.034048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 498
88.1%
1 67
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 498
88.1%
1 67
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 498
88.1%
1 67
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 498
88.1%
1 67
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 498
88.1%
1 67
 
11.9%

qualified_10
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
1
280 
0
205 
-
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 280
49.6%
0 205
36.3%
- 80
 
14.2%

Length

2024-10-31T20:56:15.171607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:15.307354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 280
49.6%
0 205
36.3%
80
 
14.2%

Most occurring characters

ValueCountFrequency (%)
1 280
49.6%
0 205
36.3%
- 80
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 280
49.6%
0 205
36.3%
- 80
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 280
49.6%
0 205
36.3%
- 80
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 280
49.6%
0 205
36.3%
- 80
 
14.2%

final_televote_points
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct172
Distinct (%)52.3%
Missing236
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean90.93921
Minimum0
Maximum439
Zeros9
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:15.481132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.4
Q126
median59
Q3129
95-th percentile254.2
Maximum439
Range439
Interquartile range (IQR)103

Descriptive statistics

Standard deviation87.021913
Coefficient of variation (CV)0.95692401
Kurtosis1.5686294
Mean90.93921
Median Absolute Deviation (MAD)42
Skewness1.3863488
Sum29919
Variance7572.8134
MonotonicityNot monotonic
2024-10-31T20:56:15.684691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
1.6%
16 7
 
1.2%
18 7
 
1.2%
5 6
 
1.1%
21 6
 
1.1%
24 6
 
1.1%
15 5
 
0.9%
59 5
 
0.9%
53 5
 
0.9%
40 4
 
0.7%
Other values (162) 269
47.6%
(Missing) 236
41.8%
ValueCountFrequency (%)
0 9
1.6%
2 4
0.7%
3 4
0.7%
4 1
 
0.2%
5 6
1.1%
6 2
 
0.4%
7 1
 
0.2%
8 3
 
0.5%
9 2
 
0.4%
10 4
0.7%
ValueCountFrequency (%)
439 1
0.2%
378 1
0.2%
376 2
0.4%
361 1
0.2%
353 1
0.2%
343 1
0.2%
337 1
0.2%
332 1
0.2%
323 1
0.2%
318 1
0.2%

final_jury_points
Real number (ℝ)

High correlation  Missing 

Distinct181
Distinct (%)55.0%
Missing236
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean90.987842
Minimum0
Maximum382
Zeros5
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:15.885955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q136
median71
Q3126
95-th percentile249.8
Maximum382
Range382
Interquartile range (IQR)90

Descriptive statistics

Standard deviation74.228675
Coefficient of variation (CV)0.81580872
Kurtosis1.4737918
Mean90.987842
Median Absolute Deviation (MAD)43
Skewness1.2606182
Sum29935
Variance5509.8962
MonotonicityNot monotonic
2024-10-31T20:56:16.094921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 7
 
1.2%
43 6
 
1.1%
69 6
 
1.1%
90 5
 
0.9%
0 5
 
0.9%
104 5
 
0.9%
114 4
 
0.7%
46 4
 
0.7%
15 4
 
0.7%
3 4
 
0.7%
Other values (171) 279
49.4%
(Missing) 236
41.8%
ValueCountFrequency (%)
0 5
0.9%
1 3
0.5%
3 4
0.7%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 2
 
0.4%
9 2
 
0.4%
10 1
 
0.2%
ValueCountFrequency (%)
382 1
0.2%
356 1
0.2%
340 1
0.2%
320 1
0.2%
312 1
0.2%
306 1
0.2%
296 1
0.2%
286 1
0.2%
283 1
0.2%
278 1
0.2%

final_televote_votes
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct45
Distinct (%)18.9%
Missing327
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean16.247899
Minimum0
Maximum151
Zeros10
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:16.302524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14.25
median11
Q327
95-th percentile38.15
Maximum151
Range151
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation16.257212
Coefficient of variation (CV)1.0005732
Kurtosis19.258555
Mean16.247899
Median Absolute Deviation (MAD)8
Skewness2.9536661
Sum3867
Variance264.29694
MonotonicityNot monotonic
2024-10-31T20:56:16.496920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
4 15
 
2.7%
3 13
 
2.3%
6 12
 
2.1%
2 11
 
1.9%
38 11
 
1.9%
11 11
 
1.9%
12 11
 
1.9%
1 11
 
1.9%
0 10
 
1.8%
7 10
 
1.8%
Other values (35) 123
 
21.8%
(Missing) 327
57.9%
ValueCountFrequency (%)
0 10
1.8%
1 11
1.9%
2 11
1.9%
3 13
2.3%
4 15
2.7%
5 8
1.4%
6 12
2.1%
7 10
1.8%
8 9
1.6%
9 6
 
1.1%
ValueCountFrequency (%)
151 1
 
0.2%
79 1
 
0.2%
57 1
 
0.2%
42 1
 
0.2%
41 3
 
0.5%
40 3
 
0.5%
39 2
 
0.4%
38 11
1.9%
37 5
0.9%
36 3
 
0.5%

final_jury_votes
Real number (ℝ)

High correlation  Missing 

Distinct44
Distinct (%)18.5%
Missing327
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean16.105042
Minimum0
Maximum90
Zeros5
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:16.695239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median14
Q322
95-th percentile35
Maximum90
Range90
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.408717
Coefficient of variation (CV)0.77048648
Kurtosis7.3955112
Mean16.105042
Median Absolute Deviation (MAD)8
Skewness1.9537756
Sum3833
Variance153.97626
MonotonicityNot monotonic
2024-10-31T20:56:16.881272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
9 12
 
2.1%
10 12
 
2.1%
11 11
 
1.9%
5 11
 
1.9%
7 10
 
1.8%
12 9
 
1.6%
21 9
 
1.6%
4 9
 
1.6%
6 9
 
1.6%
3 8
 
1.4%
Other values (34) 138
24.4%
(Missing) 327
57.9%
ValueCountFrequency (%)
0 5
0.9%
1 4
 
0.7%
2 7
1.2%
3 8
1.4%
4 9
1.6%
5 11
1.9%
6 9
1.6%
7 10
1.8%
8 8
1.4%
9 12
2.1%
ValueCountFrequency (%)
90 1
 
0.2%
71 1
 
0.2%
69 1
 
0.2%
65 1
 
0.2%
39 1
 
0.2%
38 2
0.4%
37 1
 
0.2%
36 1
 
0.2%
35 4
0.7%
34 4
0.7%

final_place
Real number (ℝ)

High correlation  Missing 

Distinct27
Distinct (%)7.5%
Missing207
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean13.391061
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:17.059172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q320
95-th percentile25
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.4302077
Coefficient of variation (CV)0.55486324
Kurtosis-1.1777307
Mean13.391061
Median Absolute Deviation (MAD)6
Skewness0.0073751022
Sum4794
Variance55.207987
MonotonicityNot monotonic
2024-10-31T20:56:17.242497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11 15
 
2.7%
12 14
 
2.5%
8 14
 
2.5%
16 14
 
2.5%
9 14
 
2.5%
6 14
 
2.5%
15 14
 
2.5%
21 14
 
2.5%
19 14
 
2.5%
7 14
 
2.5%
Other values (17) 217
38.4%
(Missing) 207
36.6%
ValueCountFrequency (%)
1 14
2.5%
2 14
2.5%
3 14
2.5%
4 13
2.3%
5 13
2.3%
6 14
2.5%
7 14
2.5%
8 14
2.5%
9 14
2.5%
10 14
2.5%
ValueCountFrequency (%)
27 1
 
0.2%
26 10
1.8%
25 14
2.5%
24 14
2.5%
23 13
2.3%
22 13
2.3%
21 14
2.5%
20 14
2.5%
19 14
2.5%
18 14
2.5%

final_total_points
Real number (ℝ)

High correlation  Missing 

Distinct231
Distinct (%)64.5%
Missing207
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean172.70391
Minimum0
Maximum758
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:17.424699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q176
median129.5
Q3229
95-th percentile474.85
Maximum758
Range758
Interquartile range (IQR)153

Descriptive statistics

Standard deviation138.46271
Coefficient of variation (CV)0.80173468
Kurtosis2.0777033
Mean172.70391
Median Absolute Deviation (MAD)68.5
Skewness1.434185
Sum61828
Variance19171.923
MonotonicityNot monotonic
2024-10-31T20:56:17.619043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 5
 
0.9%
170 5
 
0.9%
93 5
 
0.9%
120 4
 
0.7%
6 4
 
0.7%
71 4
 
0.7%
200 4
 
0.7%
65 3
 
0.5%
132 3
 
0.5%
73 3
 
0.5%
Other values (221) 318
56.3%
(Missing) 207
36.6%
ValueCountFrequency (%)
0 1
 
0.2%
3 1
 
0.2%
5 2
0.4%
6 4
0.7%
8 1
 
0.2%
10 1
 
0.2%
11 2
0.4%
13 1
 
0.2%
14 1
 
0.2%
16 1
 
0.2%
ValueCountFrequency (%)
758 1
0.2%
690 1
0.2%
639 1
0.2%
631 1
0.2%
625 1
0.2%
615 1
0.2%
583 1
0.2%
534 1
0.2%
529 1
0.2%
527 1
0.2%

semi_place
Real number (ℝ)

High correlation  Missing 

Distinct19
Distinct (%)3.9%
Missing82
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean9.1925466
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:17.785488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q313.5
95-th percentile17
Maximum19
Range18
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.0698632
Coefficient of variation (CV)0.5515189
Kurtosis-1.1596206
Mean9.1925466
Median Absolute Deviation (MAD)4
Skewness0.044237869
Sum4440
Variance25.703513
MonotonicityNot monotonic
2024-10-31T20:56:17.940519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6 28
 
5.0%
5 28
 
5.0%
1 28
 
5.0%
13 28
 
5.0%
4 28
 
5.0%
14 28
 
5.0%
15 28
 
5.0%
2 28
 
5.0%
3 28
 
5.0%
7 28
 
5.0%
Other values (9) 203
35.9%
(Missing) 82
14.5%
ValueCountFrequency (%)
1 28
5.0%
2 28
5.0%
3 28
5.0%
4 28
5.0%
5 28
5.0%
6 28
5.0%
7 28
5.0%
8 28
5.0%
9 28
5.0%
10 27
4.8%
ValueCountFrequency (%)
19 4
 
0.7%
18 14
2.5%
17 21
3.7%
16 26
4.6%
15 28
5.0%
14 28
5.0%
13 28
5.0%
12 28
5.0%
11 27
4.8%
10 27
4.8%

semi_televote_points
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct127
Distinct (%)52.3%
Missing322
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean68.263374
Minimum0
Maximum204
Zeros7
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:18.124644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1
Q124.5
median54
Q3104.5
95-th percentile162.9
Maximum204
Range204
Interquartile range (IQR)80

Descriptive statistics

Standard deviation50.617148
Coefficient of variation (CV)0.74149788
Kurtosis-0.57118772
Mean68.263374
Median Absolute Deviation (MAD)38
Skewness0.59710313
Sum16588
Variance2562.0956
MonotonicityNot monotonic
2024-10-31T20:56:18.336854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 8
 
1.4%
0 7
 
1.2%
54 6
 
1.1%
14 5
 
0.9%
93 4
 
0.7%
51 4
 
0.7%
103 4
 
0.7%
8 4
 
0.7%
41 4
 
0.7%
135 4
 
0.7%
Other values (117) 193
34.2%
(Missing) 322
57.0%
ValueCountFrequency (%)
0 7
1.2%
1 1
 
0.2%
2 2
 
0.4%
3 3
0.5%
4 2
 
0.4%
5 1
 
0.2%
6 2
 
0.4%
7 1
 
0.2%
8 4
0.7%
9 2
 
0.4%
ValueCountFrequency (%)
204 1
0.2%
202 1
0.2%
197 1
0.2%
194 1
0.2%
180 1
0.2%
177 1
0.2%
174 2
0.4%
173 1
0.2%
170 1
0.2%
165 1
0.2%

semi_jury_points
Real number (ℝ)

High correlation  Missing 

Distinct116
Distinct (%)54.7%
Missing353
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean67.575472
Minimum0
Maximum222
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:18.543565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.55
Q126.75
median58
Q398.25
95-th percentile155
Maximum222
Range222
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation47.545328
Coefficient of variation (CV)0.70358855
Kurtosis-0.20227251
Mean67.575472
Median Absolute Deviation (MAD)34.5
Skewness0.71935828
Sum14326
Variance2260.5583
MonotonicityNot monotonic
2024-10-31T20:56:18.765798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 6
 
1.1%
29 5
 
0.9%
56 4
 
0.7%
38 4
 
0.7%
16 4
 
0.7%
9 4
 
0.7%
47 3
 
0.5%
53 3
 
0.5%
92 3
 
0.5%
71 3
 
0.5%
Other values (106) 173
30.6%
(Missing) 353
62.5%
ValueCountFrequency (%)
0 1
 
0.2%
1 2
0.4%
4 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 4
0.7%
10 2
0.4%
11 3
0.5%
12 3
0.5%
ValueCountFrequency (%)
222 1
0.2%
199 1
0.2%
188 1
0.2%
174 1
0.2%
173 1
0.2%
171 1
0.2%
169 1
0.2%
167 1
0.2%
157 1
0.2%
156 1
0.2%

semi_total_points
Real number (ℝ)

High correlation  Missing 

Distinct208
Distinct (%)43.1%
Missing82
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean97.78882
Minimum0
Maximum403
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-10-31T20:56:18.968050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q143
median77
Q3134
95-th percentile238.9
Maximum403
Range403
Interquartile range (IQR)91

Descriptive statistics

Standard deviation73.844662
Coefficient of variation (CV)0.75514422
Kurtosis1.4004075
Mean97.78882
Median Absolute Deviation (MAD)41
Skewness1.2000938
Sum47232
Variance5453.0341
MonotonicityNot monotonic
2024-10-31T20:56:19.196737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 11
 
1.9%
63 9
 
1.6%
45 8
 
1.4%
67 6
 
1.1%
118 6
 
1.1%
28 6
 
1.1%
110 5
 
0.9%
41 5
 
0.9%
75 5
 
0.9%
44 5
 
0.9%
Other values (198) 417
73.8%
(Missing) 82
 
14.5%
ValueCountFrequency (%)
0 3
0.5%
1 2
0.4%
2 1
 
0.2%
3 1
 
0.2%
4 2
0.4%
6 2
0.4%
7 3
0.5%
8 4
0.7%
10 1
 
0.2%
11 4
0.7%
ValueCountFrequency (%)
403 1
0.2%
396 1
0.2%
370 1
0.2%
342 1
0.2%
337 1
0.2%
330 1
0.2%
325 1
0.2%
291 2
0.4%
288 1
0.2%
287 1
0.2%

favourite_10
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
548 
1
 
14
unknown
 
3

Length

Max length7
Median length1
Mean length1.0318584
Min length1

Characters and Unicode

Total characters583
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 548
97.0%
1 14
 
2.5%
unknown 3
 
0.5%

Length

2024-10-31T20:56:19.394933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:19.533100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 548
97.0%
1 14
 
2.5%
unknown 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 548
94.0%
1 14
 
2.4%
n 9
 
1.5%
u 3
 
0.5%
k 3
 
0.5%
o 3
 
0.5%
w 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 548
94.0%
1 14
 
2.4%
n 9
 
1.5%
u 3
 
0.5%
k 3
 
0.5%
o 3
 
0.5%
w 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 548
94.0%
1 14
 
2.4%
n 9
 
1.5%
u 3
 
0.5%
k 3
 
0.5%
o 3
 
0.5%
w 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 548
94.0%
1 14
 
2.4%
n 9
 
1.5%
u 3
 
0.5%
k 3
 
0.5%
o 3
 
0.5%
w 3
 
0.5%

race
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
unknown
562 
0
 
3

Length

Max length7
Median length7
Mean length6.9681416
Min length1

Characters and Unicode

Total characters3937
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 562
99.5%
0 3
 
0.5%

Length

2024-10-31T20:56:19.682562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:19.817155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 562
99.5%
0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 1686
42.8%
u 562
 
14.3%
k 562
 
14.3%
o 562
 
14.3%
w 562
 
14.3%
0 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3937
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1686
42.8%
u 562
 
14.3%
k 562
 
14.3%
o 562
 
14.3%
w 562
 
14.3%
0 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3937
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1686
42.8%
u 562
 
14.3%
k 562
 
14.3%
o 562
 
14.3%
w 562
 
14.3%
0 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3937
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1686
42.8%
u 562
 
14.3%
k 562
 
14.3%
o 562
 
14.3%
w 562
 
14.3%
0 3
 
0.1%

host_10
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing3
Missing (%)0.5%
Memory size4.5 KiB
0.0
548 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 548
97.0%
1.0 14
 
2.5%
(Missing) 3
 
0.5%

Length

2024-10-31T20:56:19.959498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T20:56:20.148832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 548
97.5%
1.0 14
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1110
65.8%
. 562
33.3%
1 14
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1110
65.8%
. 562
33.3%
1 14
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1110
65.8%
. 562
33.3%
1 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1110
65.8%
. 562
33.3%
1 14
 
0.8%

Interactions

2024-10-31T20:55:59.494416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:28.708599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:30.749276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.997055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.946327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.862911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.130323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.016670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.086363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:45.389741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.319546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:49.275380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:51.354346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:55.133704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.600584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:59.630988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:28.852251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:30.882259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:33.130547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.079096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.988778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.251071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.168488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.227389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:45.527200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.463303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:49.414614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:51.486861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:55.267720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.730033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:59.759733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:28.992570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:31.007826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:33.252758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.207622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:37.119527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.374495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.301547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.344419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:45.649072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.593256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:49.543300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:51.612746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:55.397597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.841700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:59.897919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:29.120540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:31.136423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:33.375231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.339850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:37.257231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.492327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.436537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.468567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:45.772998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.718535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:49.686003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:51.747616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:55.515044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.961225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:00.027750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:29.252169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:31.263905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:33.500678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.468968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:37.684613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.616427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.569550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.592291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:45.895561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.844155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:49.817816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:51.877849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:55.641023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.072484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:00.168263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:29.377708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:31.628817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:33.616268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.592455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:37.829458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.745579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.699538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.722635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.006969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.962063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:49.957039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.006078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:55.760111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.187415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:00.289990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:29.494874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:31.754532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:33.741057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.708266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:37.951860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.862252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.833513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.832726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.134544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.086284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:50.090237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.129075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:56.354462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.306886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:00.428311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:29.649247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:31.911549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:33.878916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.845680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.088486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:39.997436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:41.973038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:43.970590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.279930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.228588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:50.238370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.259942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:56.507467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.450598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:00.567557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:29.786272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.045489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.006395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:35.981879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.223119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:40.114492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:42.111251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:44.106927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.402943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.352517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:50.381131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.397639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:56.628023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.573027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:00.729794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:29.904776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.169230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.125337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.098514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.346937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:40.254385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:42.251743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:44.230168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.515570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.470327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:50.502907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.518775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:56.773875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.715536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:00.900053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:30.033403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.304676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.249511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.224591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.468301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:40.381104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:42.384610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:44.349165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.647508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.596051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:50.640487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.653927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:56.907787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.841259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:01.093811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:30.188897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.468644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.398747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.355058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.600259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:40.519663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:42.525166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:44.480459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.781364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.722949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:50.779738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.794894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.051611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:58.977724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:01.295619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:30.325956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.611137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.532655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.482489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.730631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:40.631158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:42.660275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:44.602123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:46.897205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.863357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:50.906311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:52.921314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.205139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:59.102689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:01.488627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:30.473073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.743392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.678417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.615705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.869175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:40.758820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:42.793063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:45.120737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.055903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:48.991048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:51.051092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:54.874198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.350086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:59.238357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:56:01.650929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:30.591016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:32.856331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:34.805515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:36.733874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:38.992257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:40.880167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:42.948587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:45.250175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:47.199921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:49.127973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:51.193239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:54.994003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:57.467765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T20:55:59.360920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-31T20:56:20.321776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
backing_dancersbacking_instrumentsbacking_singerscountrydirect_qualifier_10favourite_10final_draw_positionfinal_jury_pointsfinal_jury_votesfinal_placefinal_televote_pointsfinal_televote_votesfinal_total_pointsgenderhost_10instrument_10instrumentalnesskeyloudnessmain_singersqualified_10racesemi_draw_positionsemi_finalsemi_jury_pointssemi_placesemi_televote_pointssemi_total_pointsspeechinessstyleyear
backing_dancers1.000-0.296-0.1030.1510.0970.0130.000-0.083-0.058-0.0010.0740.0350.0070.0000.0720.1100.0000.1320.000-0.1440.1000.0000.0730.038-0.014-0.0810.1140.0940.1480.1350.046
backing_instruments-0.2961.000-0.1100.1630.0640.0000.000-0.088-0.1080.094-0.075-0.043-0.1000.1440.0000.1690.0000.0000.090-0.0280.0650.0000.0000.007-0.1070.101-0.039-0.1570.0980.261-0.147
backing_singers-0.103-0.1101.0000.1010.0290.0000.000-0.080-0.1700.097-0.019-0.083-0.1000.0930.1940.0510.0000.0760.000-0.0730.0580.0000.0000.000-0.0460.0600.077-0.1490.0630.034-0.345
country0.1510.1630.1011.0000.6560.0000.0750.1160.3530.1810.0000.3860.0750.0000.0000.0760.0000.0000.2450.0370.6580.0000.2090.6190.1250.1210.1390.1010.0340.0790.000
direct_qualifier_100.0970.0640.0290.6561.0000.0910.6710.1160.0000.2330.0000.0000.1560.0220.3880.0000.1610.1490.0470.0000.9710.0750.7080.7060.6450.9220.7610.6690.1460.0560.000
favourite_100.0130.0000.0000.0000.0911.0000.0490.3950.2680.3520.5050.2520.5100.0310.0000.0000.3730.0000.0430.0000.0910.9990.0000.0000.2820.2620.2860.3290.0000.0000.132
final_draw_position0.0000.0000.0000.0750.6710.0491.0000.2400.0000.1310.0000.0000.0940.0870.2070.0000.0000.0760.0050.0740.6990.0000.0860.2410.3190.3130.3090.2650.0000.0950.000
final_jury_points-0.083-0.088-0.0800.1160.1160.3950.2401.0000.863-0.7850.4280.5430.8180.0000.0000.0000.0000.0830.241-0.0460.1791.0000.0680.1710.814-0.6260.2620.4270.0000.103-0.086
final_jury_votes-0.058-0.108-0.1700.3530.0000.2680.0000.8631.000-0.7100.4590.4430.7180.0330.0000.1380.0000.0000.161-0.0950.0001.0000.0890.0000.800-0.5860.2360.4830.1430.099-0.029
final_place-0.0010.0940.0970.1810.2330.3520.131-0.785-0.7101.000-0.788-0.784-0.9210.0190.1310.0330.0000.0000.000-0.0220.3411.0000.1030.241-0.4280.709-0.609-0.5100.0000.0000.007
final_televote_points0.074-0.075-0.0190.0000.0000.5050.0000.4280.459-0.7881.0000.8910.8370.1070.0000.0000.0000.0000.1040.0240.0501.0000.1080.000-0.053-0.5470.7350.3770.0000.132-0.107
final_televote_votes0.035-0.043-0.0830.3860.0000.2520.0000.5430.443-0.7840.8911.0000.7810.0880.0000.1270.0000.0000.046-0.0040.0001.0000.2000.000-0.020-0.5060.7750.3680.3910.112-0.020
final_total_points0.007-0.100-0.1000.0750.1560.5100.0940.8180.718-0.9210.8370.7811.0000.0000.0280.0000.0000.0130.2140.0380.2511.0000.1120.1520.437-0.7050.6360.5590.0000.043-0.042
gender0.0000.1440.0930.0000.0220.0310.0870.0000.0330.0190.1070.0880.0001.0000.0440.1970.0000.0000.0000.4900.0230.0460.0000.0660.0520.0550.2010.0000.1480.1260.000
host_100.0720.0000.1940.0000.3880.0000.2070.0000.0000.1310.0000.0000.0280.0441.0000.0000.0000.0000.0000.0000.3881.0000.3470.3881.0001.0001.0001.0000.0000.0000.000
instrument_100.1100.1690.0510.0760.0000.0000.0000.0000.1380.0330.0000.1270.0000.1970.0001.0000.0000.0000.1120.1800.0000.0000.1310.0000.0000.0000.0000.0550.0000.1490.110
instrumentalness0.0000.0000.0000.0000.1610.3730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.1730.3080.2210.1691.0000.0490.1210.0000.1030.0000.0000.0940.0000.107
key0.1320.0000.0760.0000.1490.0000.0760.0830.0000.0000.0000.0000.0130.0000.0000.0000.1731.0000.0610.0150.1720.0890.0520.1330.0590.0840.0000.0730.1860.0950.102
loudness0.0000.0900.0000.2450.0470.0430.0050.2410.1610.0000.1040.0460.2140.0000.0000.1120.3080.0611.0000.0180.0530.0000.0000.0000.0370.0000.1310.1970.0000.0970.069
main_singers-0.144-0.028-0.0730.0370.0000.0000.074-0.046-0.095-0.0220.024-0.0040.0380.4900.0000.1800.2210.0150.0181.0000.0000.0000.0360.075-0.0640.0540.044-0.0830.1330.031-0.086
qualified_100.1000.0650.0580.6580.9710.0910.6990.1790.0000.3410.0500.0000.2510.0230.3880.0000.1690.1720.0530.0001.0000.0760.7080.7060.6930.9750.7710.7150.1610.0590.000
race0.0000.0000.0000.0000.0750.9990.0001.0001.0001.0001.0001.0001.0000.0461.0000.0001.0000.0890.0000.0000.0761.0000.0000.0000.0000.1070.0000.0000.0000.0000.225
semi_draw_position0.0730.0000.0000.2090.7080.0000.0860.0680.0890.1030.1080.2000.1120.0000.3470.1310.0490.0520.0000.0360.7080.0001.0000.6850.0750.0230.1490.0690.0000.0000.000
semi_final0.0380.0070.0000.6190.7060.0000.2410.1710.0000.2410.0000.0000.1520.0660.3880.0000.1210.1330.0000.0750.7060.0000.6851.0000.0000.0000.0000.0430.1080.0000.000
semi_jury_points-0.014-0.107-0.0460.1250.6450.2820.3190.8140.800-0.428-0.053-0.0200.4370.0521.0000.0000.0000.0590.037-0.0640.6930.0000.0750.0001.000-0.8460.5350.8630.0000.000-0.013
semi_place-0.0810.1010.0600.1210.9220.2620.313-0.626-0.5860.709-0.547-0.506-0.7050.0551.0000.0000.1030.0840.0000.0540.9750.1070.0230.000-0.8461.000-0.868-0.8640.0830.000-0.032
semi_televote_points0.114-0.0390.0770.1390.7610.2860.3090.2620.236-0.6090.7350.7750.6360.2011.0000.0000.0000.0000.1310.0440.7710.0000.1490.0000.535-0.8681.0000.8520.1080.1720.010
semi_total_points0.094-0.157-0.1490.1010.6690.3290.2650.4270.483-0.5100.3770.3680.5590.0001.0000.0550.0000.0730.197-0.0830.7150.0000.0690.0430.863-0.8640.8521.0000.0000.0520.287
speechiness0.1480.0980.0630.0340.1460.0000.0000.0000.1430.0000.0000.3910.0000.1480.0000.0000.0940.1860.0000.1330.1610.0000.0000.1080.0000.0830.1080.0001.0000.1720.060
style0.1350.2610.0340.0790.0560.0000.0950.1030.0990.0000.1320.1120.0430.1260.0000.1490.0000.0950.0970.0310.0590.0000.0000.0000.0000.0000.1720.0520.1721.0000.180
year0.046-0.147-0.3450.0000.0000.1320.000-0.086-0.0290.007-0.107-0.020-0.0420.0000.0000.1100.1070.1020.069-0.0860.0000.2250.0000.000-0.013-0.0320.0100.2870.0600.1801.000

Missing values

2024-10-31T20:56:02.026124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-31T20:56:02.799220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-31T20:56:03.265143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearsemi_finalsemi_draw_positionfinal_draw_positioncountryartist_namesong_namelanguagestyledirect_qualifier_10gendermain_singersageselectionkeyBPMenergydanceabilityhappinessloudnessacousticnessinstrumentalnesslivenessspeechinessrelease_datekey_change_10backing_dancersbacking_singersbacking_instrumentsinstrument_10qualified_10final_televote_pointsfinal_jury_pointsfinal_televote_votesfinal_jury_votesfinal_placefinal_total_pointssemi_placesemi_televote_pointssemi_jury_pointssemi_total_pointsfavourite_10racehost_10
020231120NorwayAlessandraQueen of KingsEnglishPop0Female1unknownunknownE Minor11036642310 dB580103unknownunknown40001216.052.036.011.05.0268.06.0102.0NaN102.00unknown0.0
1202312NaNMaltaThe BuskerDance (Our Own Party)EnglishPop-Male1unknownunknownF Minor1037870826 dB20184unknownunknown00200NaNNaNNaNNaNNaNNaN15.03.0NaN3.00unknown0.0
22023135SerbiaLuke BlackSamo mi se spavaSerbian, EnglishPop0Male1unknownunknownA Major10370561110 dB42325unknownunknown4000116.014.04.06.024.030.010.037.0NaN37.00unknown0.0
3202314NaNLatviaSudden LightsAijaEnglishRock-Male1unknownunknownA Minor1605556408 dB5087unknownunknown00300NaNNaNNaNNaNNaNNaN11.034.0NaN34.00unknown0.0
42023152PortugalMimicatAi cora��oPortuguesePop0Female1unknownunknownFs Minor1456366778 dB310165unknownunknown4000116.043.03.09.023.059.09.074.0NaN74.00unknown0.0
5202316NaNIrelandWild YouthWe Are OneEnglishRock-Male1unknownunknownD Major1126647127 dB6093unknownunknown00300NaNNaNNaNNaNNaNNaN12.010.0NaN10.00unknown0.0
620231725CroatiaLet 3Mama �C!CroatianPop0Male1unknownunknownC Major1357860637 dB160219unknownunknown04001112.011.020.02.013.0123.08.076.0NaN76.00unknown0.0
72023183SwitzerlandRemo ForrerWatergunEnglishBallad0Male1unknownunknownAb Minor1308768395 dB480187unknownunknown4000131.061.010.015.020.092.07.097.0NaN97.00unknown0.0
820231923IsraelNoa KirelUnicornEnglishPop0Female1unknownunknownFs Minor1308768285 dB203210unknownunknown50001185.0177.030.025.03.0362.03.0127.0NaN127.00unknown0.0
9202311018MoldovaPasha ParfeniSoarele si lunaRomanianTraditional0Male1unknownunknownAb Major1258662345 dB3601814unknownunknown0230176.020.017.04.018.096.05.0109.0NaN109.00unknown0.0
yearsemi_finalsemi_draw_positionfinal_draw_positioncountryartist_namesong_namelanguagestyledirect_qualifier_10gendermain_singersageselectionkeyBPMenergydanceabilityhappinessloudnessacousticnessinstrumentalnesslivenessspeechinessrelease_datekey_change_10backing_dancersbacking_singersbacking_instrumentsinstrument_10qualified_10final_televote_pointsfinal_jury_pointsfinal_televote_votesfinal_jury_votesfinal_placefinal_total_pointssemi_placesemi_televote_pointssemi_jury_pointssemi_total_pointsfavourite_10racehost_10
555200921513MoldovaNelly CiobanuHora din MoldovaRomanian, EnglishTraditional0Female1unknownunknownFs Minor908657525 dB40175unknownunknown4100166.093.0NaNNaN14.0159.05.0NaNNaN106.00unknown0.0
556200921619AlbaniaKejsi TolaCarry Me in Your DreamsEnglishDance0Female1unknownunknownAb Minor1286768678 dB0085unknownunknown3200181.026.0NaNNaN17.0107.07.0NaNNaN73.00unknown0.0
557200921721UkraineSvetlana LobodaBe My Valentine! (Anti-Crisis Girl)EnglishPop0Female1unknownunknownFs Minor1259261854 dB10646unknownunknown5001170.068.0NaNNaN12.0138.06.0NaNNaN80.00unknown0.0
558200921815EstoniaUrban SymphonyRandajadEstonianBallad0Female1unknownunknownF Minor1247269646 dB30334unknownunknown02311129.0124.0NaNNaN6.0253.03.0NaNNaN115.00unknown0.0
5592009219-NetherlandsThe ToppersShineEnglishPop-Male3unknownunknownGs Major1328966795 dB40129unknownunknown03000NaNNaNNaNNaNNaNNaN17.0NaNNaN11.00unknown0.0
5602009--3FrancePatricia KaasEt s'il fallait le faireFrenchBallad1Female1unknownunknownA Major925826238 dB550908unknownunknown0000-54.0164.0NaNNaN8.0218.0NaNNaNNaNNaN0unknown0.0
5612009--10RussiaAnastasiya PrikhodkoMamoRussian,�UkrainianBallad1Female1unknownunknownD Minor1309464454 dB410265unknownunknown0500-118.067.0NaNNaN11.0185.0NaNNaNNaNNaN0unknown1.0
5622009--17GermanyAlex�Swings�Oscar�Sings!Miss Kiss Kiss BangEnglishPop1Male1unknownunknownBb Minor1368962564 dB1102021unknownunknown3110-18.073.0NaNNaN20.091.0NaNNaNNaNNaN0unknown0.0
5632009--23United KingdomJade EwenIt's My TimeEnglishBallad1Female1unknownunknownE Major10531301214 dB179293unknownunknown0050-105.0223.0NaNNaN5.0328.0NaNNaNNaNNaN0unknown0.0
5642009--25SpainSoraya ArnelasLa noche es para miSpanish, EnglishDance1Female1unknownunknownF Minor1267069877 dB173143unknownunknown2300-38.09.0NaNNaN24.047.0NaNNaNNaNNaN0unknown0.0